These values showed statistically different among different subcellular locations. The volume (μm3) of mitochondrial meshes was the biggest in dendritic trees (9.97 ± 12.34), the smallest in granular-like axons (0.43 ± 0.25), and moderate in soma (6.26 ± 6.46) and silk-like axons (3.52 ± 4.29). These values showed significantly different among different subcellular locations. Finally, we found both the surface area and the volume of mitochondrial meshes in dendritic trees and soma within the Purkinje cells in PCs-Mito-GFP mice after receiving the training with the simulating long-term pilot flight concentrating increased significantly. The precise reconstruction of neuronal mitochondrial networks is extremely laborious, the present 4-step workflow powered by artificial intelligence and virtual reality reconstruction could successfully address these challenges.Mitochondrial networks are defined as a continuous matrix lumen, but the morphological feature of neuronal mitochondrial networks is not clear due to the lack of suitable analysis techniques. The aim of the present study is to develop a framework to capture and analyze the neuronal mitochondrial networks by using 4-step process composed of 2D and 3D observation, primary and secondary virtual reality (VR) analysis, with the help of artificial intelligence (AI)-powered Aivia segmentation an classifiers. In order to fulfill this purpose, we first generated the PCs-Mito-GFP mice, in which green fluorescence protein (GFP) could be expressed on the outer mitochondrial membrane specifically on the cerebellar Purkinje cells (PCs), thus all mitochondria in the giant neuronal soma, complex dendritic arborization trees and long projection axons of Purkinje cells could be easily detected under a laser scanning confocal microscope. The 4-step process resolved the complicated neuronal mitochondrial networks into discrete neuronal mitochondrial meshes. Second, we measured the two parameters of the neuronal mitochondrial meshes, and the results showed that the surface area (μm 2) of mitochondrial meshes is the biggest in dendritic trees (45.30 ± 53.21), the smallest in granular-like axons (3.99 ± 1.82), and moderate in soma (27.81 ± 22.22) and silk-like axons (17.50 ± 15.19). And these values show statistically different among different subcellular locations. At the same time, the volume (μm3) of mitochondrial meshes is the biggest in dendritic trees (9.97 ± 12.34), the smallest in granular-like axons (0.43 ± 0.25), and moderate in soma (6.26 ± 6.46) and silk-like axons (3.52 ± 4.29). And these values show significantly different among different subcellular locations. Neuronal mitochondrial meshes are the elementary units for neuronal mitochondrial networks (MNs). In the present study, it is the first time to measure two important parameters of the surface area and the volume of neuronal mitochondrial meshes by using artificial intelligence (AI)—powered classifiers and immersive virtual reality (VR) reconstruction. The results show that the surface area (μm In spec1 in R version 3.0.1 (64-bit)  . The Mit(64-bit) . Three-d(64-bit) . Integra2 in area, similar to those seen in other neurons -powered virtual reality (VR) reconstruction. In order to fulfill this purpose, we first generated the PCs-Mito-GFP mice, in which GFP could be expressed on the outer mitochondrial membrane specifically on the cerebellar Purkinje cells (PCs), thus all mitochondria in the giant neuronal soma, complex dendritic arborization trees and long projection axons of Purkinje cells could be easily detected under a laser scanning confocal microscope. The 4-step process allowed us to resolve the complicated neuronal mitochondrial networks into discrete neuronal mitochondrial meshes. More importantly, we measured the two important parameters of the neuronal mitochondrial meshes. The results showed that the surface area of mitochondrial meshes was the biggest in dendritic trees, the smallest in granular-like axons, and moderate in soma and silk-like axons. And these values show statistically different among different subcellular locations. At the same time, the volume of mitochondrial meshes is the biggest in dendritic trees, the smallest in granular-like axons, and moderate in soma and silk-like axons. And these values show significantly different among different subcellular locations. Finally, we compared both the surface area and the volume of mitochondrial meshes in dendritic trees and soma within the Purkinje cells in PCs-Mito-GFP mice with or without receiving the training with the simulating long-term pilot flight concentrating model. The results showed that the surface area and volume of mitochondrial mesh in dendritic trees or soma of Purkinje cells in PCs-Mito-GFP mice with the training increased significantly, compared with that in mice without the training, by approximately 27 and 38%, respectively. The precise reconstruction of neuronal mitochondrial networks is extremely laborious, the present 4-step workflow powered by artificial intelligence and virtual reality reconstruction could successfully address these challenges.We first generated the PCs-Mito-GFP mice, in which GFP could be expressed on the outer mitochondrial membrane specifically on the cerebellar Purkinje cells (PCs). As shown in floxed allele, the set of primers were used with wildtype F1: 5′-CCCAAAGTCGCTCTGAGTTGTTA-3′, wildtype R1: 5′-TGGCGTTACTATGGGAACATACGTC-3′ and insert F2: 5′-CCCAAAGTCGCTCTGAGTTGTTA-3′, insert F2: 5′-TCGGGTGAGCATGTCTTTAATCT-3′, resulted with two products of 479 bp and 375 bp, under the procedures of 95°C 5 min, 98°C 30 s/65°C 30 s (–0.5°C/cycle)/72°C 45 s/98°C 30 s/55°C 30 s/72°C 45 s with 20 cycles, and 72°C 5 min. As shown in floxed allele yields two bands of both 479 and 375 bp, and the target PCs-Mito-GFP allele yields three bands of 567, 479, and 375 bp. As shown in To genotype the PCs-Mito-GFP allele and PCs-Mito-GFP by PCR, the set of primers of Cre were used with F3: 5′-ATTCTCGTGGAACTGGATGG-3′,5′-GGACAGGTAATGGTTGTCTGG-3′, resulted with 567 bp product, under the procedures of 94°C 3 min, 94°C 30 s/62°C 35 s/72°C 35 s with 35 cycles, and 72°C 5 min. To genotype the wild-type allele and GFP-Mito tagAs shown in 2 For super-resolution microscopic images of Purkinje cells, Z-stack of axons, soma and dendritic trees were 141 slices (18.2 μm) at 0.13 μm interval, 93 slices (13.8 μm) at 0.15 μm interval and 89 slices (11.44 μm) at 0.13 μm interval, respectively.The first step was to acquire two-dimensional (2D) images. Firstly, we acquired the two-dimensional images, which were prepared under confocal microscopy . Aged 7 weeks PCs-Mito-GFP mice were perfused transcardially with phosphate buffered saline (PBS) followed by 4% fresh formulated paraformaldehyde (Sigma). Tissues were embedded overnight at 4°C in 30% sucrose solution. The cerebellum were isolated and frozen in OCT for sagittal sectioning by a cryostat  with 15 μm thickness, which as incubated with DAPI  for 5 min after washing in PBS. The raw z-stack images of PCs-Mito-GFP were acquired on a Zeiss LSM900 laser scanning confocal microscope equipped with Airyscan 2 system, and a 63 × 1.4 NA Oil DIC M27 objective (Zeiss). We have identified anatomical structures according to Allen map.The step 2 was to construct 3D structure based on the multiple 2D images. Three dimensional (3D) images of mitochondrial networks of cerebellar Purkinje cells were composed of all continuous 2D images of Z-stack by Aivia  . Aivia uhttps://www.vive.com/cn/setup/pc-vr/, Steam from https://store.steampowered.com/about/ and SteamVR. The VR was achieved on the support of Aivia software  from https://www.leica-microsystems.com.cn/cn/products/microscope-software/p/aivia/. ziviaVR was implemented and evaluated on computers with Intel(R) Core(TM) i9-10980XE CPU@3.00GHz, 256 GB RAM, 8TB ROM, NVIDIA Quadro RTX 6,000 GPU, Windows 10 64-bit edition, and Aivia Pro as the VR device. And this AiviaVR helped the controller to control the observation view at will and observe mitochondrial networks in an immersive and all-round way.We used virtual reality (VR) technology for immersive observation of a continuous matrix lumen shaped mitochondria networks in Purkinje cells. The VR environment was installed the, VIVE from Before the 4th step, the segmentation and classifiers were performed by using Aivia, and then the continuous matrix lumen shaped mitochondria networks could be converted to discrete neuronal mitochondrial meshes. It should be indicated that Aivia uses an artificial intelligence (AI)-based software architecture to build a complete platform for two-dimensional to five-dimensional image visualization, analysis and data interpretation that reliably processes and reconstructs highly complex images in just a few minutes. AI-powered segmentation is based on different convolutional neural network architectures  to process images. And AI-powered classification, employing object classifier, is in terms of random forest. The Aivia’s AI is characterized by enabling complex, difficult and time-consuming image processing to be completed quickly, objectively and repeatably and efficiently, even when the analyst does not have the relevant expertise. AI is able to reliably process and reconstruct highly complex images in just a few minutes. Thus, reliable and repeatable segmentation results are generated to effectively and quickly realize 2D and 3D image visualization and analysis.To achieve image visualization rapidly and analyze it accurately and repeatably, we used AI-powered Aivia software to view and analyze our mitochondrial networks. In the present procedures, to quantify the mitochondria, AI-powered segmentation, the module of Recipe Analysis, was used to mesh it, of which the steps followed Aivia’s specification of the recipes analysis module. The whole segmentation process includes two parts: detection and partition. In order to make the data reproducible, we unified the parameters of detection and partition. When detecting mitochondria, the average object radius and minimum edge fluorescence intensity were controlled within 0.7 microns and 5. And in the mitochondrial partition stage, the object radius ranged from 0.2 microns to 50 microns. The minimum distance from the center of an object to the edge that is touching its closet neighboring object was set as 20 microns. With the accurate pinpointing capability in AiviaVR –4, in reNext, Aivia AI learning was used to classify the discrete neuronal mitochondrial meshes according to the locations of soma, dendritic trees, axons. And axons were further divided into granular-like axons and silk-like axons. The meshes keeping the same position coordinates and morphological characteristics, including superficial area, volume and immunofluorescence intensity, were marked as soma “Class” through the function of AI teaching of Aivia. The module of “AI-classifier,” an object classifier, was run, of which the operation procedures were conformed to the instruction of Aivia. In our study, meshes were regarded as the object of classification. For example, we selected some meshes from the soma of Purkinje cells and incorporated them into the soma “Class,” which was a process of training. So did the meshes from dendrites and axons of Purkinje cells. Finally, the different colors are used to label meshes in different parts of Purkinje cells.Next the meshed mitochondrial networks achieved by Aivia’s AI-powered segmentation and classifiers were observed by the secondary immersive VR, specially paying attention to the locations of meshes. The secondary VR was achieved successfully by AiviaVR, and the procedures were strictly performed and complied with the instruction of Aivia. The highly complex meshes could be effectively and immersively observed using AiviaVR. We can use the VR controller to control the observation view at will, so as to observe meshes in an immersive and all-round way.To simulate the condition of a pilot with 1-year-flight, the small animal overweight simulated centrifuge machine  was used in our experiment, as shown in post-hoc test was used for multiple comparison between groups. The two samples were compared using the two-tailed Student’s t-test. The Kruskal-Wallis test or Mann-Whitney U-test were used to analyze the data that did not conform to the test of normality and homogeneity of variance. Graphpad was used for statistical analysis. P < 0.05 was considered statistically significant. All scatter graphs and line graphs were created and synthesized by Graphpad software.The data were expressed as mean ± standard deviation. First, all data were tested for normality and homogeneity of variance test. One-way ANOVA was used to evaluate the differences between the three groups for comparison, and LSD floxed) mouse line , in which a mitochondrial localized version of the enhanced green-fluorescence-protein EGFP (mito-GFP) was targeted to the ubiquitously expressed Rosa26 locus, along with an upstream loxP-flanked termination signal, by CRISPR/Cas9 strategy. We have bought B6.129-Tg(Pcp2–cre)2Mpin/J transgenic mice , who express a cre gene inserted into exon 4 of a Pcp2 gene. Recombinase activity is observed in most Purkinje cells (PCs) and some retinal bipolar neurons and is first observed around postnatal day 6 and is fully established 2–3 weeks after birth. Pcp2-Cre mice from Jackson Laboratory . Then we crossed the Mito-GFP mice with PCs-Cre mice, PCs-Mito-GFP mice were built in which the targeted LoxP allele can be mated to a Cre driver line to obtain cell-specific labeling of mitochondria. As shown in floxed strain (on the right), by using the three primers in schematic. From the results, we found that number 4 and 5 are the target mice, which were used in our research.The above results indicate that the present transgenic mice could be used for further observation.According to 4-step process , we firs2, as well as their projecting thin axons and flourish incoming dendrites could be easily distinguished and outlined. The MNs in soma could not be depicted stereoscopically.Then after Z stacked processing in depth of approximately 20 μm for total more than 80 2D images in STEP 1, 3D reconstruction for MNs in Purkinje cells of PCs-Mito-GFP mice were made.Thereafter the MNs within three subcellular units could be shown close to stereoscopically. Unfortunately, it was impossible to measure or compare MNs precisely.Then by use of Aivia’s AI-powered segmentation, we for the first time found that neuronal MNs were composed of the fundamental units, named mitochondrial meshes.Then by use of Aivia’s AI-powered classifiers, we for the first time calculated the parameters of surface area and volume of every mitochondrial mesh. 3, most of these meshes were pink mitochondrial meshes within the granular-silk axons. 3, most of these meshes were blue mitochondrial meshes within the dendritic trees.2) was the biggest in dendritic trees (45.30 ± 53.21), the smallest in granular-like axons (3.99 ± 1.82), and in the middle in soma (27.81 ± 22.22) and silk-like axons (17.50 ± 15.19). The values of mitochondrial meshes in four groups showed statistically significantly (P < 0.001).3) was also the biggest in dendritic trees (9.97 ± 12.34), the smallest in granular-like axons (0.43 ± 0.25), and in the middle in soma (6.26 ± 6.46) and silk-like axons (3.52 ± 4.29). The values of mitochondrial meshes in four groups showed statistically significantly.2 (yellow part).3 (yellow part).We also showed 50 pieces of VR images of mitochondrial meshes within each group of dendritic trees , soma S and axonBased on these results, we put forward the hypothesis that neuronal MNs were composed by regular meshes whose surface area and volume were the biggest in dendritic trees, the smallest in axons, and moderate in soma.2) when compared with that in the pre-treatment group (14.93 ± 9.60 μm2) (P < 0.001); similarly, the volume of mitochondrial meshes within the dendritic tress increased significantly in the post-treatment group (5.41 ± 5.15 μm3) when compared with that in the pre-treatment group (3.52 ± 3.30 μm3) (P < 0.001). 2) when compared with that in the pre-treatment group (23.20 ± 21.78 μm2) (P < 0.001); similarly, the volume of mitochondrial meshes within the soma increased significantly in the post-treatment group (9.86 ± 11.71 μm3) when compared with that in the pre-treatment group (7.16 ± 9.91 μm3) (P < 0.001). Thereafter we indicated the meshed neuronal MNs might be the fundamental unit best for real-time fluctuated and complex mitochondria function.It has been shown that after long-term professional training and flight practice, pilots would acquire more skilled driving skills and a much more precise sense of direction . The undIn this report, we developed a framework to capture and analyze the neuronal mitochondrial networks by using 4-step composed of 2D and 3D observation, primary and secondary virtual reality (VR) analysis, with the help of artificial intelligence (AI)-powered Aivia segmentation and classifiers. The 4-step process allowed us to propose that the neuronal mitochondrial networks might be composed of regular mitochondrial meshes, and more importantly, provide the detailed quantitative measurement of the two important parameters of the surface area and the volume of neuronal mitochondrial meshes, by using the generated PCs-Mito-GFP mice in which the mitochondria specific within the cerebellar Purkinje cells are labeled by GFP. The results showed that both the surface area and the volume of mitochondrial meshes in Purkinje cells were the biggest in dendritic trees, the smallest in granular-like axons, and moderate in soma and silk-like axons. Moreover, both the surface area and the volume of mitochondrial meshes in dendritic trees and soma within the Purkinje cells in PCs-Mito-GFP mice, who were trained by the simulating long-term pilot flight concentrating, showed significantly increased. Hence we propose that the AI-powered segmentation and classifiers, combined with the immersive VR reconstruction could resolve the complicated neuronal mitochondrial networks into discrete and quantifiable mitochondrial meshes, so as to achieve the detailed morphological analysis of neuronal mitochondrial networks.The morphology of mitochondrial networks (MNs), a term that encompasses the branched, reticular structure of fused mitochondria as well as the separate, punctate individual organelles within a eukaryotic cell. Over the past decade, the significance of the mitochondrial network has been increasingly appreciated, motivating the development of various approaches to analyze it. Protocols enabling imaging of mitochondrial morphological network are currently available for both wide-field epifluorescence microscopy and high-resolution laser scanning confocal microscopy . A custoOur research team has used VR technology for the first time to observe mitochondria in PCs. The present AiviaVR offers an immersive, intuitive, and realistic experience for exploring mitochondrial network imaging data, where real and virtual contents were synthetically put together to demonstrate the user experience of AiviaVR. While VR has not been widely used in neuroscience, it is useful for biological problems, especially due to the intrinsic multi-dimensional nature of many biological datasets, and has the potential to be integrated as the next standard protocol. AiviaVR is among the first demonstration of such utility with great potential. While immersive VR visualization of biological surface objects and sometimes also imaging data were shown in applications such as biological education and data analyses, there is little existing work on developing open-source VR software packages for very complicated and teravoxel-scale imaging datasets such as the whole-brain imagery as we have introduced here. We expect that AiviaVR can also be used to analyze other massive-scale datasets, especially changes of mitochondrial network under multiple stresses. We chose to focus on applying AiviaVR to the single-neuron reconstruction of mitochondrial network for two major reasons. First, currently no other alternative tools are able to reconstruct the fine, complicated mitochondrial network unambiguously in this way. Second, there has been little previous work on streamlining the large-scale data production of the complete mitochondrial network at single-neuron level at high precision and scale. We have uploaded all of these AiviaVR databases on our web of In order to provide an effective database for more mitochondrial researchers.It is the first time to make use of artificial intelligence (AI)-powered Aivia segmentation an classifiers to study the complicated neuronal mitochondrial networks. In front of step 4, the segmentation and classifiers have been performed by using Aivia, and then the continuous matrix lumen shaped mitochondria networks could be converted to discrete neuronal mitochondrial meshes. Aivia uses an artificial intelligence (AI)-based software architecture to build a complete platform for two-dimensional to five-dimensional image visualization, analysis, and data interpretation that reliably processes and reconstructs highly complex images in just a few minutes. AI-powered segmentation is based on different convolutional neural network architectures to process images. And AI-powered classification, employing object classifier, is in terms of random forest. The Aivia’s AI is characterized by enabling complex, difficult and time-consuming image processing to be completed quickly, objectively and repeatably and efficiently, even when the analyst does not have the relevant expertise. AI is able to reliably process and reconstruct highly complex images in just a few minutes. Thus, reliable and repeatable segmentation results are generated to effectively and quickly realize 2D and 3D image visualization and analysis. In the present research, Aivia’s use of artificial intelligence will greatly improve our efficiency in quantifying mitochondria.3. PhAMfloxed line, a mitochondrial localized version of the photo-convertible fluorescent protein Dendra2 (mito-Dendra2) is targeted to the ubiquitously expressed Rosa26 locus, along with an upstream loxP-flanked termination signal. Then they crossed PhAMfloxed mice with the Pcp2-Cre line, which drives Cre expression in Purkinje cells (PCs) of the cerebellum.Although mitochondria are often depicted as discrete organelles, they actually form a highly dynamic and interconnected networks that undergoes continuous remodeling through rounds of organelle fusion and fission . DespiteBut there are some limitations here. One is the lack of collaborative interaction, so in fact, the description is still subjective and cannot be quantified. Second, light microscope observation cannot avoid the false results due to light-induced diffraction. Third, the combination of light microscope and electron microscope should be more reliable to reveal the mitochondrial network in the future.The present study created a four-step observational system of 2D/3D/primary VR/secondary VR to provide immersive evaluation of mitochondrial networks. The results of knot-like connected mitochondrial network within the axons and the dendritic trees, as well as the hexagonal two-dimensional unit for mitochondrial network within the soma, implies the further great effort should be addressed to elucidate the mitochondrial network. Future issues contain: 1. How does the mitochondrial network respond to multiple stresses? 2. How are different shapes of mitochondrial network form? 3. How does the mitochondrial network change in the absence of important mitochondrial dynamic factors, including fission factors of Drp1, Fis1, and fusion factors of Mfn1, Mfn2…?The original contributions presented in this study are included in the article/The animal study was reviewed and approved by the Animal Care and Use Committee of the Air Force Military Medical University.Y-YW, Y-LY, FT, and LW designed the experiments. S-JL, HL, and F-FW conducted the experiments. HL, D-YF, and SZ conducted the part of AI-classification and VR-observation. S-JL, HL, and JZ analyzed the data and drew the figures. Y-YW, S-JL, and HL wrote the manuscript. All authors read and approved the final manuscript.


Brugia malayidue to the activation of TRP-2. Here we show that DEC causes transient paralysis inC. elegans at high concentrations and is 200x less potent compared to its effect onB. malayi.C. elegans trp-2(sy691) mutants are like the wild-type and only paralyzed by high concentrations of DEC. Our results demonstrate that high concentrations of DEC cause paralysis ofC. elegans independent of TRP-2.Diethylcarbamazine (DEC) has been used to treat lymphatic filariasis in tropical countries since the 1940s. Its mode of action is still unclear, with several reports suggesting a host immune system-mediated mechanism. We previously demonstrated that DEC causes transient spastic paralysis in the filarial nematode The direct effect of DEC on the whole worms was first reported in filariaB. malayi . Low (4µM) concentrations of DEC caused transient spastic paralysis due to muscle contraction in both male and female worms . Electrophysiological, genetic, and behavioral assays revealed DEC acts by targeting the TRP channels, TRP-2 in the muscle cells ofB. malayi .Diethylcarbamazine (DEC) is a classic anthelmintic used to treat diseases caused by filarial nematodes like lymphatic filariasis and loiasis. DEC is very effective in clearing the microfilaria from the blood, but the therapeutic effects are transient as the microfilariae return in the blood after a few hours (Hawking and Laurie 1949). The mode of action of DEC has not been well understood, and many reports suggest that DEC acts by stimulating the host immune system . DEC causes rapid, concentration-dependent paralysis that persists for a few hours before the worms recover. The IC50for the paralyzing effect of DEC at 30s post-treatment was 4.4±0.3mM. For this study, we first treated wild-type N2 worms with different concentrations of DEC and found that DEC was only effective at high concentrations . DEC causes transient, flaccid paralysis , and the worms start to recover 40 minutes post-treatment of 3mM DEC . We plotted the concentration-response plot for the DEC-induced paralysis 30s post-treatment and found the IC50to be 0.8±0.2mM .C. eleganshave a thicker cuticle , preventing the entry of the drug and thereby limiting its effects. We used abus-12(e2740) that has a fragile cuticle  and found DEC had similar effects compared to the N2 . The IC50forbus-12was 0.5±0.04mM, indistinguishable from N2, indicating DEC passes through the cuticle, and its lack of potency may be due to reduced effect on its target receptor.We have previously reported that DEC has paralyzing effects on the filarial nematodeB. malayi, current responses to DEC are abolished when the TRPC gene trp-2is knocked down , indicating that TRP-2 could be the putative target for DEC. TheC. elegans genome encodes for three TRPC genes,trp-1,trp-2, andspe-41,whereas the parasitic worm genomes encodetrp-2 andspe-41. SPE-41 is enriched in the male sperm head  and may not play a role in DEC-mediated paralysis. Phylogenetic analysis in parasite wormbase (www.parasite.wormbase.org) suggests thatC. elegans'TRP-2 is the closest ortholog to the filarial TRP-2s and is present in a different node to TRP-1. To test if DEC acts through TRP-2 inC. elegans, we usedtrp-2(sy691), a null allele , and assayed the effects of DEC on motility. We observed that DEC caused transient paralysis in thetrp-2mutants similar to the N2 strains with an IC50of 0.4±0.1mM Fig 1G and H). Our results suggest that, unlike filaria, TRP-2 might not be the putative target for higher concentrations of DEC in the free-living nematodeC. elegans.InC. elegans at higher concentrations independent of TRP-2. TRP-2 is expressed in the body wall muscle ofB. malayi and not inC. elegans,where it is expressed neuronally . According to Wormbase, most of the TRP channels inC. elegansare expressed neuronally or in the intestine. The DEC activation of TRP channels in these tissues might not be sufficient to cause potent paralysis. Also, DEC might be targeting a different TRP channel in the muscle cells ofC. eleganswith a lower potency. Our results suggest DEC has a parasite-specific effect in paralyzing the worm, and it causes transient paralysis at very high concentrations inC. elegans, independent of its putative parasitic target TRP-2.Our results demonstrate that DEC causes transient paralysis inStrains:bus-12(e2977),andtrp-2(sy691), were obtained from the Caenorhabditis Genetic Centre . AllC. eleganshermaphrodites were maintained on 60mm Petri dishes containing standard NGM, spread with E. coli OP50 as food stored at 20ºC. Well-fed, motile young adult worms, approximately three days after hatching, were used in all experiments.Strains used in this study, Wild-type (Bristol N2),Drugs:Diethylcarbamazine citrate  used in this study was obtained from Sigma Aldrich . 100mM stock DEC was prepared by dissolving in distilled water and stored at -20°C.Motility assays:C. eleganswere treated with various concentrations of DEC . Worm motility was recorded for 30s before the addition of DEC, 30s following the addition of DEC and at 2, 40, and 120-min post-treatment to generate a concentration- and time-course response analysis. Motility was recorded for 30s for all time points.Motility assays were performed on young adult hermaphrodites. Fifteen worms from each strain were transferred to a 96 well plate (1 worm/well) containing RPMI media at a final volume of 200µL. After allowing 20 min for acclimatization, DEC was added and video recordings were performed using WormLab software with a purpose-built illumination stand (MBF Bioscience) equipped with a Nikon 60 mm Microlens  and an AVT Stingray F-504B digital camera . Videos were captured at 7.5 frames per second and saved as AVI files. To determine the potency of DEC on young adult hermaphrodite,Data Analysis and Statistics:Caenorhabditis elegansto determine the average speed of the 45 individual worms (n=15 per strain). Wormlab analyzed, frame by frame, the detected changes in movement to track the total length of forward and reverse movement in micrometers and the average speed of the worm in micrometers per second (μm/s). The data were analyzed using GraphPad Prism 5.0 software .We used WormLab software  to track and analyze the recorded videos of the


Hospitalized patients mainly included adults suffering from humoral immunodeficiencies requiring immunoglobulin replacement therapy and as expected had lower B cell counts compared to non-hospitalized patients. Infection fatality rate in the whole cohort was 3.5%. Seroconversion was observed is 86.6% of the patients evaluated and in 83.3% of CVID patients. 16.85% of the patients reported long-lasting COVID symptoms. All but one patient with prolonged symptoms were under IgRT. The fatality rate observed in IEI was slightly similar to the general population. The age of the patients who did not survive was lower compared to the general population, and the age stratified mortality in the 50–60 age range considerable exceeded the mortality from 50 to 60 age group of the Italian population . We hypothesize that this is due to the fact that comorbidities in IEI patients are very common and usually appear early in life.COVID-19 manifestations range from asymptomatic to life-threatening infections. The outcome in different inborn errors of immunity (IEI) is still a matter of debate. In this retrospective study, we describe the experience of the of the Italian Primary Immunodeficiencies Network (IPINet). Sixteen reference centers for adult or pediatric IEI were involved. One hundred fourteen patients were enrolled including 35 pediatric and 79 adult patients. Median age was 32 years, and male-to-female ratio was 1.5:1. The most common IEI were 22q11.2 deletion syndrome in children (26%) and common variable immunodeficiency (CVID) in adults (65%). Ninety-one patients did not require hospital admission, and among these, 33 were asymptomatic. Hospitalization rate was 20.17%. Older age (The online version contains supplementary material available at 10.1007/s10875-022-01264-y. Many aspects of the infection still need to be clarified in this syndrome. These include the ability to clear the infection in different IEI and the ability to develop an effective immunological memory post-infection.During the last 2 years, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide, causing about 5 million deaths. Clinical manifestations are very variable, going from a completely asymptomatic infection to multiorgan failure and death . The maiIn this paper, we describe the clinical course and outcome in a cohort of IEI patients followed at centers of the of the Italian Primary Immunodeficiencies Network (IPINet) who experienced SARS-CoV-2 infection between March 2020 and April 2021. The aim of the study is to better define the clinical course of the infection and define risk factors for a worse outcome in a large cohort of patients.n° 13, 18, 25, 38, 40–45, 58, 64, 86, and 99–101 in the supplemental table. In this study, we performed an in-depth evaluation including detailed information on the type of underlying IEI, sex, age, comorbidities, and ongoing therapies along with baseline immunological and lung function assessment. As for the baseline immunological assessment, information collected included immunoglobulin levels, number and percentage of white blood cells (WBC), neutrophils, lymphocytes, T lymphocytes (CD3 +), helper T cells (CD4 +), cytotoxic T cells (CD8 +), B cells (CD19 +), and NK cells (CD16 + CD56 +). As for the lung function, information collected included baseline SatO2% and pre-existing structural lung damage . To evaluate the duration of the viral shedding, the dates of the first positive and first negative nasopharyngeal swab were recorded. The patients were then stratified according to hospitalization. For patients not requiring hospitalization, information collected included body temperature, resting SatO2%, SatO2% after 6-min walking test (6MWT), and type and duration of the symptoms including cough, headache, sore throat, nasal obstruction, anosmia/ageusia, myalgia, nausea/vomiting, diarrhea, loss of appetite, conjunctivitis, dyspnea, increased expectorate, and hemoptoe. The average time to recovery from COVID-19 is highly variable and depends on age and pre-existing comorbidities and illness severity [Patients in follow-up at IPINet centers who experienced SARS-CoV-2 infection between March 2020 and April 2021 were included in the study. SARS-CoV-2 infection was confirmed by RT-PCR on the nasopharyngeal swab or through antibody testing. For each patient, data were collected retrospectively from the clinical records. The survey was sent to 16 Italian reference centers for adult  or pediatric  IEI. Most of the cases have been already included in another study on the IPINet cohort focusing on incidence, outcome, and infection-fatality rate [severity , 8. In iseverity , 8. We et test for independent samples and Fisher’s exact or chi-square test were used to compare means and proportions, respectively. Values of P < 0.05 were considered statistically significant.The statistical software packages GraphPad and Excel were used for the statistical analyses and to produce all the graphs. All the values are showed as means ± standard deviation. Two tail One hundred fourteen patients were included into the study. In most of the cases 108/114), the diagnosis of SARS-CoV-2 infection was confirmed through RT-PCR on the nasopharyngeal swab. In the remaining cases (6/114), a previous infection was identified during routine antibody testing during follow-up visits. Median age was 32 years (mean 33.2 ± 20.1 years), and male-to-female ratio was 1.5:1. The cohort included 35 pediatric and 79 adult patients. In the pediatric cohort, 22q11.2 deletion syndrome (DS) represented the most common IEI (26%), followed by unclassified antibody deficiency , X-linked agammaglobulinemia , and Wiskott-Aldrich  , UAD (3), agammaglobulinemia (3), CID 2), 22q11.2DS (2), WAS post-BMT (2), APDS (1), ataxia telangiectasia (1), hyperIgM (1), immune dysregulation (1), Myd88 deficiency (1), post-HSCT SCID (1), and SIgAD (1). Mean age in asymptomatic patients was 29.85 ± 19.56 years , and male-to-female ratio was 3.6:1. We compared the rate of asymptomatic patients in our cohort with that observed in cohorts of patients with IEI previously published. Interestingly, the rate of asymptomatic patients in our cohort was significantly higher compared to the international cohort published by Meyts et al. , 22q11.2, while n, UAD (3)p 0.01) . These included patients with different IEI, namely CVID (7), XLA (2), SCID (NBAS deficiency) (1), cDGS (1), Good’s syndrome (1), UAD (2), and SIgAD (1). All the patients with prolonged infection, except the patient with SIgAD deficiency, were under IgRT. The prevalence of prolonged infection was 16% in CVID and 40% in XLA.Twenty-five patients required hospital admission 21.92%). These included 2 children who were admitted for medical reasons different from COVID-19, who were excluded from the following analysis. Thus, the hospitalization rate was 20.17%. As expected, the hospitalization rate was higher in the adult compared to the pediatric cohort , and non-hospitalized patients were significantly younger than hospitalized patients . Moreover, patients who died were older than survivors  even though this difference was not statistically significant. Immunological risk factors for hospital admission were represented by lower baseline B cell count and IgRT. However, this is likely due to the fact that hospitalized patients were mainly older and suffered from humoral immunodeficiencies. On the contrary, baseline lymphocyte, neutrophil, CD3 + lymphocyte, CD4 + lymphocyte, CD8 + lymphocyte, and CD16 + CD56 + counts were comparable between hospitalized and non-hospitalized patients and between survivors and non-survivors , 1 patient with neutropenia, 2 patients with combined immunodeficiency, 1 patient with Good’s syndrome under IgRT, 1 patient with NK deficiency, 1 patient with EO-IBD, and 1 patient with SIgAD). In 4 patients, there was no seroconversion, and these included patients with Good’s syndrome, cDGS, agammaglobulinemia, and CVID. All the patients without seroconversion, except for the patient with Good’s syndrome, were under IgRT. Seroconversion was observed in 5/6 patients with CVID (83.3%).p 0.0065)  . Viral shedding was longer in hospitalized compared to non-hospitalized patients . Low molecular weight heparin was used in 7 cases. Immunosuppressive drugs were used in 10 cases and included steroids in 10 cases, anakinra in one pediatric patient prolonged infection and tocilizumab in one case. Hyperimmune plasma was used to treat 3 patients, two of whom affected with prolonged infection and casirivimab/imdevimab was used in 2 patients with relapsing COVID.In the period March 2020 to April 2021, a total of 114 patients infected by SARS-CoV-2, including both children and adults with IEI, were identified in the enrolled centers. Most of the pediatric patients suffered from 22q11.2DS, while most of the adults suffered from humoral immunodeficiencies and in particular CVID. This distribution parallels the distribution of IEI cases in the two groups.Differently from some of the previously published IEI cohorts, most of the patients (80%) in the present study were managed at home and did not require hospital admission , 13, 15.In our cohort, asymptomatic patients included patients with different IEI. Interestingly, among the asymptomatic patients, we also identified a patient with Myd88 deficiency. The patient is of Romani ethnicity and is affected with a classical early onset form caused by a homozygous c.192_194delGGA (p.Glu66del) mutation, associated with a clinical history of severe infections. This was quite surprising in that innate immune response is critical for recognizing and controlling infections through the release of cytokines and chemochines. In SARS-CoV-2 infection, hyperactive cytokine release and cytokine storm are associated with the development of severe forms. A recent study shows that TLR2 and Myd88 are implicated in SARS-CoV-2-induced inflammatory response and are associated with COVID-19 disease severity . This woMean age of the asymptomatic patients was not significantly lower compared to that of patients with mild-moderate symptoms. The most common symptom reported in patients not requiring hospital admission was fever followed by general symptoms and respiratory symptoms. Gastrointestinal symptoms were more rarely observed, and, differently from what observed in the general population, there was no difference among adults and children in the incidence of gastrointestinal symptoms , 22. In p 0.7246), and the mean age of deceased people was much lower in our cohort (48 ± 12.52 vs 80 years). We also compared the age stratified mortality, and we found that the mortality in the 50–60 age range considerable exceeds the mortality from 50 to 60 age group of the Italian population  [https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_15-dicembre-2021.pdf). This might be due to the fact that compared to the general population IEI patients have more comorbidities, usually appearing at a younger age. Almost half of the patients in our cohort had preexisting lung disease. No-one under the age of 30 in the IEI cohort died. This is in keeping with other studies that suggest age is the biggest risk factor.As expected, hospitalization rate was higher in adults than in children, and in the whole cohort, mean age of hospitalized patients was significantly higher than non-hospitalized. In the pediatric cohort, only 2 children required hospital admission: a 1-year-old patient with cDGS and a 2.75-year-old child with NBAS deficiency . Both pap < 0.0001). Similarly, a longer viral shedding was observed in hospitalized compared to non-hospitalized patients. Among the patients with prolonged infection, all but one patient with SIgAD were under IgRT, and patients with profound humoral immunodeficiency also showed a longer viral shedding compared to other IEI. This confirms what reported in previous studies from the literature showing that persistent infection or relapsing–remitting infection is associated with profound humoral immunodeficiency and the failure to make de novo humoral responses to SARS-CoV-2 [In the general population, the average time to recovery from COVID-19 is highly variable and depends on age and pre-existing comorbidities in addition to illness severity , 8. IndiRS-CoV-2 . In one RS-CoV-2 . No concRS-CoV-2 . Longer RS-CoV-2 –32. The RS-CoV-2 . The EurRS-CoV-2 . Mira etRS-CoV-2 . In our The rapid diffusion of the pandemic prompted studies on the development of new vaccination strategies. Patients with disorders of the immune system were vaccinated with priority. However, information on the efficacy of the vaccine in this population is still limited. We evaluated seroconversion after natural infection in 30 cases. Due to the difference in the techniques used for the analysis in the different labs, we were not able to compare the titer among the patients. Seroconversion was observed in most cases (86.6%). As recently shown in papers looking at the seroconversion after SARS-CoV-2 vaccination in IEI , 37, we In conclusion, in this study, we reported on the clinical course and outcome of SARS-CoV-2 infection in a large cohort of IEI patients, identified over a long period of observation. The identification of the patients through different phases of the pandemics has shown that, similarly to the general population, the clinical course in these patients is often very mild or asymptomatic. In fact, because of the redundancy of the immune system, only a few immunodeficiencies are associated with an increased risk of severe COVID. These include autoimmune polyendocrine syndrome type 1 and the immunodeficiencies involving the type I IFN pathway –44. The Supplementary file1 (DOCX 63 KB)Below is the link to the electronic supplementary material.